Oleic acid‐bound <scp>FABP7</scp> drives glioma cell proliferation through regulation of nuclear lipid droplet formation
Bibliographic record
Abstract
Fatty acid-binding protein 7 (FABP7), one of the fatty acid (FA) chaperones involved in the modulation of intracellular FA metabolism, is highly expressed in glioblastoma, and its expression is associated with decreased patients' prognosis. Previously, we demonstrated that FABP7 requires its binding partner to exert its function and that a mutation in the FA-binding site of FABP7 affects tumour biology. Here, we explored the role of FA ligand binding for FABP7 function in tumour proliferation and examined the mechanism of FABP7 and ligand interaction in tumour biology. We discovered that among several FA treatment, oleic acid (OA) boosted cell proliferation of FABP7-expressing cells. In turn, OA increased FABP7 nuclear localization, and the accumulation of FABP7-OA complex in the nucleus induced the formation of nuclear lipid droplet (nLD), as well as an increase in colocalization of nLD with promyelocytic leukaemia (PML) nuclear bodies. Furthermore, OA increased mRNA levels of proliferation-related genes in FABP7-expressing cells through histone acetylation. Interestingly, these OA-boosted functions were abrogated in FABP7-knockout cells and mutant FABP7-overexpressing cells. Thus, our findings suggest that FABP7-OA intracellular interaction may modulate nLD formation and the epigenetic status thereby enhancing transcription of proliferation-regulating genes, ultimately driving tumour cell proliferation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".